NDLI: Neural network data-driven engine torque and air-fuel ratio control

Content Provider	IEEE Xplore Digital Library
Author	Gerasimov, D.N. Pshenichnikova, E.I.
Copyright Year	2012
Description	Author affiliation: Saint-Petersburg State University of Information, Technologies, Mechanics and Optics, Saint-Petersburg, Russia (Gerasimov, D.N.; Pshenichnikova, E.I.)
Abstract	In this paper the problem of simultaneous torque tracking and AFR stabilization in spark ignition (SI) engines at the stoichiometric level is addressed. To provide a suitable solution for this problem, an approach based on artificial neural networks with adaptive tuning of their synaptic weights is proposed. For the design of the control law, empirical mathematical models of air-fuel ratio (AFR) and torque are generated and verified. The models are presented as grey boxes with chosen input variables and parameters identified. Identification of the parameters and further verification of the models are provides with the use of data obtained during a driving cycle for a vehicle equipped with a V8 engine. The dual channel neural network controller is tuned in on-line operation according to error back propagation algorithm and the performance index directly depending on the control errors. The controller is free from noise amplification and is robust with respect to uncontrollable engine disturbances. Simulation results show excellent performance of the controller over the entire range of operating conditions.
Starting Page	524
Ending Page	527
File Size	450779
Page Count	4
File Format	PDF
ISBN	9781467307826
ISSN	21588473
e-ISBN	9781467307840
DOI	10.1109/MELCON.2012.6196487
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-25
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Engines Torque Biological neural networks Silicon Input variables Fuels Control systems
Content Type	Text
Resource Type	Article

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